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Kuan-Yu Jin; Wai-Lok Siu – Journal of Educational Measurement, 2025
Educational tests often have a cluster of items linked by a common stimulus ("testlet"). In such a design, the dependencies caused between items are called "testlet effects." In particular, the directional testlet effect (DTE) refers to a recursive influence whereby responses to earlier items can positively or negatively affect…
Descriptors: Models, Test Items, Educational Assessment, Scores
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Nianbo Dong; Benjamin Kelcey; Jessaca Spybrook; Yanli Xie; Dung Pham; Peilin Qiu; Ning Sui – Grantee Submission, 2024
Multisite trials that randomize individuals (e.g., students) within sites (e.g., schools) or clusters (e.g., teachers/classrooms) within sites (e.g., schools) are commonly used for program evaluation because they provide opportunities to learn about treatment effects as well as their heterogeneity across sites and subgroups (defined by moderating…
Descriptors: Statistical Analysis, Randomized Controlled Trials, Educational Research, Effect Size
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Ö. Emre C. Alagöz; Thorsten Meiser – Educational and Psychological Measurement, 2024
To improve the validity of self-report measures, researchers should control for response style (RS) effects, which can be achieved with IRTree models. A traditional IRTree model considers a response as a combination of distinct decision-making processes, where the substantive trait affects the decision on response direction, while decisions about…
Descriptors: Item Response Theory, Validity, Self Evaluation (Individuals), Decision Making
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Kyung-Mi O. – Language Testing in Asia, 2024
This study examines the efficacy of artificial intelligence (AI) in creating parallel test items compared to human-made ones. Two test forms were developed: one consisting of 20 existing human-made items and another with 20 new items generated with ChatGPT assistance. Expert reviews confirmed the content parallelism of the two test forms.…
Descriptors: Comparative Analysis, Artificial Intelligence, Computer Software, Test Items
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Roger Young; Emily Courtney; Alexander Kah; Mariah Wilkerson; Yi-Hsin Chen – Teaching of Psychology, 2025
Background: Multiple-choice item (MCI) assessments are burdensome for instructors to develop. Artificial intelligence (AI, e.g., ChatGPT) can streamline the process without sacrificing quality. The quality of AI-generated MCIs and human experts is comparable. However, whether the quality of AI-generated MCIs is equally good across various domain-…
Descriptors: Item Response Theory, Multiple Choice Tests, Psychology, Textbooks
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Mimi Ismail; Ahmed Al - Badri; Said Al - Senaidi – Journal of Education and e-Learning Research, 2025
This study aimed to reveal the differences in individuals' abilities, their standard errors, and the psychometric properties of the test according to the two methods of applying the test (electronic and paper). The descriptive approach was used to achieve the study's objectives. The study sample consisted of 74 male and female students at the…
Descriptors: Achievement Tests, Computer Assisted Testing, Psychometrics, Item Response Theory
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Boxuan Ma; Sora Fukui; Yuji Ando; Shinichi Konomi – Journal of Educational Data Mining, 2024
Language proficiency diagnosis is essential to extract fine-grained information about the linguistic knowledge states and skill mastery levels of test takers based on their performance on language tests. Different from comprehensive standardized tests, many language learning apps often revolve around word-level questions. Therefore, knowledge…
Descriptors: Language Proficiency, Brain Hemisphere Functions, Language Processing, Task Analysis